azure-architecture-autopilot
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads azure-architecture-autopilot automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Azure Architecture Builder
A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.
The diagram engine is embedded within the skill (scripts/ folder).
No pip install needed — it directly uses the bundled Python scripts
to generate interactive HTML diagrams with 605+ official Azure icons.
Ready to use immediately without network access or package installation.
Automatic User Language Detection
🚨 Detect the language of the user's first message and provide all subsequent responses in that language. This is the highest-priority principle.
- If the user writes in Korean → respond in Korean
- If the user writes in English → respond in English (ask_user, progress updates, reports, Bicep comments — all in English)
- The instructions and examples in this document are written in English, and all user-facing output must match the user's language
⚠️ Do not copy examples from this document verbatim to the user. Use only the structure as reference, and adapt text to the user's language.
Tool Usage Guide (GHCP Environment)
| Feature | Tool Name | Notes |
|---|---|---|
| Fetch URL content | web_fetch | For MS Docs lookups, etc. |
| Web search | web_search | URL discovery |
| Ask user | ask_user | choices must be a string array |
| Sub-agents | task | explore/task/general-purpose |
| Shell command execution | powershell | Windows PowerShell |
All sub-agents (explore/task/general-purpose) cannot use
web_fetchorweb_search. Fact-checking that requires MS Docs lookups must be performed directly by the main agent.
External Tool Path Discovery
az, python, bicep, etc. are often not on PATH.
Discover once before starting a Phase and cache the result. Do not re-discover every time.
⚠️ Do not use
Get-Command python— risk of Windows Store alias. Direct filesystem discovery ($env:LOCALAPPDATA\Programs\Python) takes priority.
az CLI path:
$azCmd = $null
if (Get-Command az -ErrorAction SilentlyContinue) { $azCmd = 'az' }
if (-not $azCmd) {
$azExe = Get-ChildItem -Path "$env:ProgramFiles\Microsoft SDKs\Azure\CLI2\wbin", "$env:LOCALAPPDATA\Programs\Azure CLI\wbin" -Filter "az.cmd" -ErrorAction SilentlyContinue | Select-Object -First 1 -ExpandProperty FullName
if ($azExe) { $azCmd = $azExe }
}
Python path + embedded diagram engine: refer to the diagram generation section in references/phase1-advisor.md.
Progress Updates Required
Use blockquote + emoji + bold format:
> **⏳ [Action]** — [Reason]
> **✅ [Complete]** — [Result]
> **⚠️ [Warning]** — [Details]
> **❌ [Failed]** — [Cause]
Parallel Preload Principle
While waiting for user input via ask_user, preload information needed for the next step in parallel.
| ask_user Question | Preload Simultaneously |
|---|---|
| Project name / scan scope | Reference files, MS Docs, Python path discovery, diagram module path verification |
| Model/SKU selection | MS Docs for next question choices |
| Architecture confirmation | az account show/list, az group list |
| Subscription selection | az group list |
Path Branching — Automatically Determined by User Request
Path A: New Design (New Build)
Trigger: "create", "set up", "deploy", "build", etc.
Phase 1 (references/phase1-advisor.md) — Interactive architecture design + diagram
↓
Phase 2 (references/bicep-generator.md) — Bicep code generation
↓
Phase 3 (references/bicep-reviewer.md) — Code review + compilation verification
↓
Phase 4 (references/phase4-deployer.md) — validate → what-if → deploy
Path B: Existing Analysis + Modification (Analyze & Modify)
Trigger: "analyze", "current resources", "scan", "draw a diagram", "show my infrastructure", etc.
Phase 0 (references/phase0-scanner.md) — Existing resource scan + diagram
↓
Modification conversation — "What would you like to change here?" (natural language modification request → follow-up questions)
↓
Phase 1 (references/phase1-advisor.md) — Confirm modifications + update diagram
↓
Phase 2~4 — Same as above
When Path Determination Is Ambiguous
Ask the user directly:
ask_user({
question: "What would you like to do?",
choices: [
"Design a new Azure architecture (Recommended)",
"Analyze + modify existing Azure resources"
]
})
Phase Transition Rules
- Each Phase reads and follows the instructions in its corresponding
references/*.mdfile - When transitioning between Phases, always inform the user about the next step
- Do not skip Phases (especially the what-if between Phase 3 → Phase 4)
- 🚨 Required condition for Phase 1 → Phase 2 transition:
01_arch_diagram_draft.htmlmust have been generated using the embedded diagram engine and shown to the user. Do not proceed to Bicep generation without a diagram. Completing spec collection alone does not mean Phase 1 is done — Phase 1 includes diagram generation + user confirmation. - Modification request after deployment → return to Phase 1, not Phase 0 (Delta Confirmation Rule)
Service Coverage & Fallback
Optimized Services
Microsoft Foundry, Azure OpenAI, AI Search, ADLS Gen2, Key Vault, Microsoft Fabric, Azure Data Factory, VNet/Private Endpoint, AML/AI Hub
Other Azure Services
All supported — MS Docs are automatically consulted to generate at the same quality standard. Do not send messages that cause user anxiety such as "out of scope" or "best-effort".
Stable vs Dynamic Information Handling
| Category | Handling Method | Examples |
|---|---|---|
| Stable | Reference files first | isHnsEnabled: true, PE triple set |
| Dynamic | Always fetch MS Docs | API version, model availability, SKU, region |
Quick Reference
| File | Role |
|---|---|
references/phase0-scanner.md | Existing resource scan + relationship inference + diagram |
references/phase1-advisor.md | Interactive architecture design + fact checking |
references/bicep-generator.md | Bicep code generation rules |
references/bicep-reviewer.md | Code review checklist |
references/phase4-deployer.md | validate → what-if → deploy |
references/service-gotchas.md | Required properties, PE mappings |
references/azure-dynamic-sources.md | MS Docs URL registry |
references/azure-common-patterns.md | PE/security/naming patterns |
references/ai-data.md | AI/Data service guide |
assets/06-architecture-diagram.png | Example generated architecture diagram |
assets/07-azure-portal-resources.png | Example Azure portal resource view |
assets/08-deployment-succeeded.png | Example successful deployment result |
FAQ
What does the azure-architecture-autopilot skill do?
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
How do I install azure-architecture-autopilot?
Run npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/azure-architecture-autopilot/ (personal) or .claude/skills/azure-architecture-autopilot/ (project).
Which AI agents can use azure-architecture-autopilot?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is azure-architecture-autopilot safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains azure-architecture-autopilot?
It is published by github in github/awesome-copilot, and it is an official skill from the maker of the technology it covers. It has 1,483 recorded installs.
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